Systems and methods for predicting information handling resource failures using deep recurrent neural network with a modified gated recurrent unit having missing data imputation

ABSTRACT

A method may include receiving telemetry data associated with one or more information handling resources, receiving failure statistics associated with the one or more information handling resources, merging the telemetry data and the failure statistics to create training data, and implementing a gated recurrent unit to: (i) impute missing values from the training data and (ii) train a pattern recognition engine configured to predict a failure status of an information handling resource from operational data associated with the information handling resource.

TECHNICAL FIELD

The present disclosure relates in general to information handlingsystems, and more particularly to methods and systems for predictinginformation handling resource failures using a deep recurrent neuralnetwork having a modified gated recurrent unit capable of imputingmissing training data, and performing imputation for training, test, andprediction steps.

BACKGROUND

As the value and use of information continues to increase, individualsand businesses seek additional ways to process and store information.One option available to users is information handling systems. Aninformation handling system generally processes, compiles, stores,and/or communicates information or data for business, personal, or otherpurposes thereby allowing users to take advantage of the value of theinformation. Because technology and information handling needs andrequirements vary between different users or applications, informationhandling systems may also vary regarding what information is handled,how the information is handled, how much information is processed,stored, or communicated, and how quickly and efficiently the informationmay be processed, stored, or communicated. The variations in informationhandling systems allow for information handling systems to be general orconfigured for a specific user or specific use such as financialtransaction processing, airline reservations, enterprise data storage,or global communications. In addition, information handling systems mayinclude a variety of hardware and software components that may beconfigured to process, store, and communicate information and mayinclude one or more computer systems, data storage systems, andnetworking systems.

Many information handling resources, in particular hard disk drives andbatteries, may suffer from faults or failures that require replacement.However, replacement of such devices after failure or fault may beundesirable as it leads to system downtime. Accordingly, systems andmethods for predicting component failure in order to enable pre-failurefailure placement of information handling systems is desired.

One approach to predict component failure is given by U.S. patentapplication Ser. No. 15/861,039, which uses a long-short term memory(LSTM) recurrent neural network. However, the approach disclosed in U.S.patent application Ser. No. 15/861,039 may have disadvantages. Forexample, telemetry data may be collected at irregular frequencies,wherein the time between collections may be inconsistent. In addition,recorded telemetry data may have fields missing at random. In addition,data imputation methods employed by the LSTM approach (e.g., discretecosine transformation) disclosed in U.S. patent application Ser. No.15/861,039 may not be scalable to large data sets, as cosine transformapproach may require many elements within the telemetry data in order torepresent the signals. In addition, the LSTM approach requires a stepfor imputation followed by a step for training.

SUMMARY

In accordance with the teachings of the present disclosure, thedisadvantages and problems associated with addressing failures ofinformation handling resources in an information handling system may bereduced or eliminated.

In accordance with embodiments of the present disclosure, an informationhandling system may include a processor and a non-transitorycomputer-readable medium having stored thereon a program of instructionsexecutable by the processor. Program of instructions may be configuredto, when read and executed by the processor, receive telemetry dataassociated with one or more information handling resources, receivefailure statistics associated with the one or more information handlingresources, merge the telemetry data and the failure statistics to createtraining data, and implement a gated recurrent unit to: (i) imputemissing values from the training data and (ii) train a patternrecognition engine configured to predict a failure status of aninformation handling resource from operational data associated with theinformation handling resource.

In accordance with these and other embodiments of the presentdisclosure, a method may include receiving telemetry data associatedwith one or more information handling resources, receiving failurestatistics associated with the one or more information handlingresources, merging the telemetry data and the failure statistics tocreate training data, and implementing a gated recurrent unit to: (i)impute missing values from the training data and (ii) train a patternrecognition engine configured to predict a failure status of aninformation handling resource from operational data associated with theinformation handling resource.

In accordance with these and other embodiments of the presentdisclosure, an article of manufacture may include a non-transitorycomputer-readable medium and computer-executable instructions carried onthe computer readable medium, the instructions readable by a processor.The instructions, when read and executed, may cause the processor toreceive telemetry data associated with one or more information handlingresources, receive failure statistics associated with the one or moreinformation handling resources, merge the telemetry data and the failurestatistics to create training data, and implement a gated recurrent unitto: (i) impute missing values from the training data; and (ii) train apattern recognition engine configured to predict a failure status of aninformation handling resource from operational data associated with theinformation handling resource.

Technical advantages of the present disclosure may be readily apparentto one skilled in the art from the figures, description and claimsincluded herein. The objects and advantages of the embodiments will berealized and achieved at least by the elements, features, andcombinations particularly pointed out in the claims.

It is to be understood that both the foregoing general description andthe following detailed description are examples and explanatory and arenot restrictive of the claims set forth in this disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete understanding of the present embodiments and advantagesthereof may be acquired by referring to the following description takenin conjunction with the accompanying drawings, in which like referencenumbers indicate like features, and wherein:

FIG. 1 illustrates a block diagram of an example client informationhandling system, in accordance with embodiments of the presentdisclosure;

FIG. 2 illustrates a block diagram of an example system for predictinginformation handling resource failures, in accordance with embodimentsof the present disclosure;

FIG. 3 illustrates a functional block diagram of the central supportengine depicted in FIG. 2, in accordance with embodiments of the presentdisclosure; and

FIG. 4 illustrates a functional block diagram of a gated recurrent unit,in accordance with embodiments of the present disclosure.

DETAILED DESCRIPTION

Preferred embodiments and their advantages are best understood byreference to FIGS. 1 through 4, wherein like numbers are used toindicate like and corresponding parts.

For the purposes of this disclosure, an information handling system mayinclude any instrumentality or aggregate of instrumentalities operableto compute, classify, process, transmit, receive, retrieve, originate,switch, store, display, manifest, detect, record, reproduce, handle, orutilize any form of information, intelligence, or data for business,scientific, control, entertainment, or other purposes. For example, aninformation handling system may be a personal computer, a personaldigital assistant (PDA), a consumer electronic device, a network storagedevice, or any other suitable device and may vary in size, shape,performance, functionality, and price. The information handling systemmay include memory, one or more processing resources such as a centralprocessing unit (“CPU”) or hardware or software control logic.Additional components of the information handling system may include oneor more storage devices, one or more communications ports forcommunicating with external devices as well as various input/output(“I/O”) devices, such as a keyboard, a mouse, and a video display. Theinformation handling system may also include one or more buses operableto transmit communication between the various hardware components.

For the purposes of this disclosure, computer-readable media may includeany instrumentality or aggregation of instrumentalities that may retaindata and/or instructions for a period of time. Computer-readable mediamay include, without limitation, storage media such as a direct accessstorage device (e.g., a hard disk drive or floppy disk), a sequentialaccess storage device (e.g., a tape disk drive), compact disk, CD-ROM,DVD, random access memory (RAM), read-only memory (ROM), electricallyerasable programmable read-only memory (EEPROM), and/or flash memory; aswell as communications media such as wires, optical fibers, microwaves,radio waves, and other electromagnetic and/or optical carriers; and/orany combination of the foregoing.

For the purposes of this disclosure, information handling resources maybroadly refer to any component system, device or apparatus of aninformation handling system, including without limitation processors,service processors, basic input/output systems (BIOSs), buses, memories,I/O devices and/or interfaces, storage resources, network interfaces,motherboards, and/or any other components and/or elements of aninformation handling system.

FIG. 1 illustrates a block diagram of an example client informationhandling system 102, in accordance with embodiments of the presentdisclosure. In some embodiments, client information handling system 102may comprise a server. In other embodiments, client information handlingsystem 102 may be a personal computer (e.g., a desktop computer, alaptop, notebook, tablet, handheld, smart phone, personal digitalassistant, etc.). As depicted in FIG. 1, client information handlingsystem 102 may include a processor 103, a memory 104 communicativelycoupled to processor 103, a storage medium 106 communicatively coupledto processor 103, a basic input/output system (BIOS) 105 communicativelycoupled to processor 103, a network interface 108 communicativelycoupled to processor 103, and one or more other information handlingresources 120 communicatively coupled to processor 103.

Processor 103 may include any system, device, or apparatus configured tointerpret and/or execute program instructions and/or process data, andmay include, without limitation, a microprocessor, microcontroller,digital signal processor (DSP), application specific integrated circuit(ASIC), or any other digital or analog circuitry configured to interpretand/or execute program instructions and/or process data. In someembodiments, processor 103 may interpret and/or execute programinstructions and/or process data stored in memory 104, storage medium106, BIOS 105, and/or another component of client information handlingsystem 102.

Memory 104 may be communicatively coupled to processor 103 and mayinclude any system, device, or apparatus configured to retain programinstructions and/or data for a period of time (e.g., computer-readablemedia). Memory 104 may include RAM, EEPROM, a PCMCIA card, flash memory,magnetic storage, opto-magnetic storage, or any suitable selectionand/or array of volatile or non-volatile memory that retains data afterpower to client information handling system 102 is turned off.

Storage medium 106 may be communicatively coupled to processor 103 andmay include any system, device, or apparatus operable to storeinformation processed by processor 103. Storage medium 106 may include,for example, network attached storage, one or more direct access storagedevices (e.g., hard disk drives), and/or one or more sequential accessstorage devices (e.g., tape drives). As shown in FIG. 1, storage medium106 may have stored thereon an operating system (OS) 114, and a clientsupport engine 116.

OS 114 may be any program of executable instructions, or aggregation ofprograms of executable instructions, configured to manage and/or controlthe allocation and usage of hardware resources such as memory, CPU time,disk space, and input and output devices, and provide an interfacebetween such hardware resources and application programs hosted by OS114. Active portions of OS 114 may be transferred to memory 104 forexecution by processor 103.

Client support engine 116 may comprise a program of instructionsconfigured to, when loaded into memory 104 and executed by processor103, perform one or more tasks related to collection and communication(e.g., via network interface 108) of telemetry information associatedwith information handling resources of client information handlingsystem 102 (including, without limitation, storage medium 106 andinformation handling resources 120), as is described in greater detailelsewhere in this disclosure.

BIOS 105 may be communicatively coupled to processor 103 and may includeany system, device, or apparatus configured to identify, test, and/orinitialize information handling resources of client information handlingsystem 102. “BIOS” may broadly refer to any system, device, or apparatusconfigured to perform such functionality, including without limitation,a Unified Extensible Firmware Interface (UEFI). In some embodiments,BIOS 105 may be implemented as a program of instructions that may beread by and executed on processor 103 to carry out the functionality ofBIOS 105. In these and other embodiments, BIOS 105 may comprise bootfirmware configured to be the first code executed by processor 103 whenclient information handling system 102 is booted and/or powered on. Aspart of its initialization functionality, code for BIOS 105 may beconfigured to set components of client information handling system 102into a known state, so that one or more applications (e.g., operatingsystem 114 or other application programs) stored on compatible media(e.g., memory 104, storage medium 106) may be executed by processor 103and given control of client information handling system 102.

Network interface 108 may include any suitable system, apparatus, ordevice operable to serve as an interface between client informationhandling system 102 and a network external to client informationhandling system 102 (e.g., network 210 depicted in FIG. 2). Networkinterface 108 may allow client information handling system 102 tocommunicate via an external network using any suitable transmissionprotocol and/or standard.

Generally speaking, information handling resources 120 may include anycomponent system, device or apparatus of client information handlingsystem 102, including without limitation processors, buses,computer-readable media, input-output devices and/or interfaces, storageresources, network interfaces, motherboards, electro-mechanical devices(e.g., fans), displays, batteries, and/or power supplies.

FIG. 2 illustrates a block diagram of an example system 200 forpredicting information handling resource failures, in accordance withembodiments of the present disclosure. As shown in FIG. 2, system 200may include a plurality of client information handling systems 102 (suchas those depicted in FIG. 1), a central information handling system 202,and a network 210 communicatively coupled to client information handlingsystems 102 and central information handling system 202.

In some embodiments, central information handling system 202 maycomprise a server. In other embodiments, central information handlingsystem 202 may be a personal computer (e.g., a desktop computer, alaptop, notebook, tablet, handheld, smart phone, personal digitalassistant, etc.). As depicted in FIG. 2, central information handlingsystem 202 may include a processor 203, a memory 204 communicativelycoupled to processor 203, a storage medium 206 communicatively coupledto processor 203, and a network interface 208 communicatively coupled toprocessor 203.

Processor 203 may include any system, device, or apparatus configured tointerpret and/or execute program instructions and/or process data, andmay include, without limitation, a microprocessor, microcontroller,digital signal processor (DSP), application specific integrated circuit(ASIC), or any other digital or analog circuitry configured to interpretand/or execute program instructions and/or process data. In someembodiments, processor 203 may interpret and/or execute programinstructions and/or process data stored in memory 204, storage medium206, and/or another component of client information handling system 202.

Memory 204 may be communicatively coupled to processor 203 and mayinclude any system, device, or apparatus configured to retain programinstructions and/or data for a period of time (e.g., computer-readablemedia). Memory 204 may include RAM, EEPROM, a PCMCIA card, flash memory,magnetic storage, opto-magnetic storage, or any suitable selectionand/or array of volatile or non-volatile memory that retains data afterpower to client information handling system 202 is turned off.

Storage medium 206 may be communicatively coupled to processor 203 andmay include any system, device, or apparatus operable to storeinformation processed by processor 203. Storage medium 206 may include,for example, network attached storage, one or more direct access storagedevices (e.g., hard disk drives), and/or one or more sequential accessstorage devices (e.g., tape drives). As shown in FIG. 2, storage medium206 may have stored thereon an operating system (OS) 214, and a centralsupport engine 216.

OS 214 may be any program of executable instructions, or aggregation ofprograms of executable instructions, configured to manage and/or controlthe allocation and usage of hardware resources such as memory, CPU time,disk space, and input and output devices, and provide an interfacebetween such hardware resources and application programs hosted by OS214. Active portions of OS 214 may be transferred to memory 204 forexecution by processor 203.

Central support engine 216 may comprise a program of instructionsconfigured to, when loaded into memory 204 and executed by processor203, perform one or more tasks related to receipt of telemetryinformation from client information handling systems 102, receipt ofdata regarding actual failure of information handling resources, andcorrelate such telemetry information and failure information to predictthe occurrence of failures of information handling resources of clientinformation handling systems 102, as is described in greater detailelsewhere in this disclosure.

Network interface 208 may include any suitable system, apparatus, ordevice operable to serve as an interface between central informationhandling system 202 and network 210. Network interface 208 may allowcentral information handling system 202 to communicate via an externalnetwork using any suitable transmission protocol and/or standard.

In addition to or in lieu of one or more of processor 203, memory 204,storage medium 206, and network interface 208, central informationhandling system 202 may comprise one or more other information handlingresources.

Network 210 may comprise a network and/or fabric configured to coupleinformation handling systems of system 200 (e.g., client informationhandling systems 102 and central information handling system 202) to oneanother. Thus, central information handling system 202 may be able toaccess, via network 210, telemetry data collected and communicated byclient support engines 116 executing on client information handlingsystems 102.

FIG. 3 illustrates a functional block diagram of central support engine216 depicted in FIG. 2, in accordance with embodiments of the presentdisclosure. As shown in FIG. 3, central support engine 216 may implementan input processing unit 302, a recurrent neural network with modifiedgated recurrent unit (RNN/GRU) 304 having missing data imputation, and arule-based decision engine 306.

Input processing unit 302 may receive telemetry data from clientinformation handling systems 102 and may also receive failure statisticsregarding client information handling systems 102. Such telemetry datamay include any operational data associated with an information handlingresource of a client information handling system 102. For example,telemetry data may include information regarding performance of aninformation handling resource, environmental conditions associated withan information handling resource, or any other suitable operational dataregarding an information handling resource. As a specific example,telemetry data for a hard disk drive may include information regardingcyclic redundancy check errors, volume of read input/output, volume ofwrite input/output, operating temperature, rotation rate of rotationalmedia, number of power cycles, amount of time the hard disk drive ispowered on, and/or other parameters. Failure statistics may include, foreach information handling resource from which telemetry data isreceived, an indication of a failure status of the information handlingresource (e.g., failed, about to fail, healthy). In some embodiments,failure statistics may be received from a repair and/or servicingfacility that may manually or automatically inspect information handlingresources for their health status.

Input processing unit 302 may merge telemetry data and the failurestatistics to create one or more labeled time series patterns, which itmay output to RNN/GRU 304 as training data. Input processing unit 302may generate the time series patterns to have any suitable length andmay sample telemetry data and failure statistics at any appropriatesampling frequency.

RNN/GRU 304 may receive the time series data as training data, such thatRNN/GRU 304 may perform as a pattern recognition engine. Thus, inoperation, once trained, RNN/GRU 304 may monitor telemetry data frominformation handling resources of client information handling systems102 and predict a failure status (e.g., failed, about to fail, healthy)based on pattern analysis of the telemetry data. Accordingly, RNN/GRU304 may predict a failure of an information handling resource before itactually occurs. As explained in greater detail below, RNN/GRU 304 maybe unable to handle any uneven time gaps in the sample or the timeseries of its training data, thus imputing missing data from thetraining data in order to perform training and prediction.

Based on the failure status, rules-based decision engine 306 maygenerate a decision for one or more information handling resources basedon the predicted failure status. Rules applied by rules-based decisionengine 306 may consider warranty status of an information handlingresource, criticality of the information handling resource,service/support level of the information handling resource, and/or anyother suitable factor. For information handling resources predicted tohave a status of failed or about to fail, the decision generated byrules-based decision engine 306 may comprise any remedial action to betaken in response to the status, including dispatch of a replacementinformation handling resource, dispatch of a technician to repair orreplace the information handling resource, and/or communication of analert regarding the information handling resource.

FIG. 4 illustrates a functional block diagram of a gated recurrent unit400, in accordance with embodiments of the present disclosure. A gatedrecurrent unit (GRU) may perform functions similar to LSTM, but with afewer number of steps. GRUs may be computationally less expensive whencompared to LSTMs and may be fine-tuned to achieve similar levels ofaccuracy. GRU 400 may comprise a cell, a remember gate, and an updategate. GRU 400, unlike an LSTM, may not have a forget gate and may neednot store a cell state. Accordingly, compared to LSTM, GRU 400 may havelower computational requirements as it may eliminate the processingrequired to calculate the forget gate and the storage required tomaintain the cell state. GRU 400 may calculate the future state based onthe last output and the current input.

As background, a multivariate time series with D variables of length Tmay be denoted as:

X=(x ₁ ,x ₂ , . . . , x _(T))^(T) ∈ R ^(T×D).

where for each t ∈ 1, 2, . . . , T, x_(t) ∈ R^(D) represents the t-thobservation of all variables and x_(t) ^(d) denotes the d-th variable ofx_(t). s_(t) ∈ R denotes the time-stamp for the t-th observation ands1=0, for all variables. To keep track of the missing values, a maskingvector m_(t) ∈ (0; 1)D, which is 0 for missing values and 1 otherwise.Another vector δ_(t) ^(d) ∈ R, may be used to maintain the time intervalsince the last observation. Mathematically, such vectors may be writtenas:

$m_{t}^{d} = \left\{ {{\begin{matrix}1 & {{if}\mspace{14mu} x_{t}^{d}\mspace{14mu} {is}\mspace{14mu} {observed}} \\0 & {otherwise}\end{matrix}\delta_{t}^{d}} = \left\{ \begin{matrix}{{s_{t} - s_{t - 1} + \delta_{t - 1}^{d}},} & {{t > 1},{m_{t - 1}^{d} = 0}} \\{{s_{t} - s_{t - 1}},} & {{t > 1},{m_{t - 1}^{d} = 1}} \\{0,} & {t = 1}\end{matrix} \right.} \right.$

A GRU such as that shown in FIG. 4 may be mathematically written as,

R _(t)=σ(W _(r) [X _(t) , h _(t−1) ]+B _(r))

Z _(t)=σ(W _(z) [X _(t) , h _(t−1) ]+B _(z))

h′ _(t)=tan h(W _(h) [X _(t) , R _(t) ⊙h _(t−1) ]+B _(h))

h _(t)=(1−Z _(t))⊙h _(t−1) +Z _(t) ⊙h′ _(t)

wherein: (i) R_(t) and Z_(t) are reset and update gates for the t^(th)time period, respectively; (ii) h_(t)′ and h_(t) are the input andoutput for the t^(th) time period and comprise the information added tothe cell using the update gate; (iii) W and B are weights and biasmatrices with subscripts r and z pertaining to input and update,respectively; and (iv) σ and tank are the sigmoid and the hyperbolictangent activation functions. In operation, h_(t) may be passed to afully-connected output layer, to calculate the output for the t^(th)time-period. The output from the output layer may be the estimate of theresponse variable for the t_(th) time period and may be used tocalculate the loss and initiate the gradient for back-propagation.

GRU 400 may be further modified in order to train variables so as tolearn distributions of predictor variables, by adding weight matrices tothe GRU equations and modifying input variables. For example, a decayrate may be used to modify the inputs and the hidden state. Such decayrate may be given by:

γ_(t)=exp[−max(0, W _(γ)δ_(t) +b _(γ))],

where W_(Y) and b_(Y) may be trained jointly with all other parametersof GRU 400. In some embodiments, two versions of the decay functiongiven above may be used. The first decay function may be used to modifyinputs to GRU 400 and may be given by:

{circumflex over (x)} _(t) ^(d) =m _(t) ^(d) x _(t) ^(d)+(1−m _(t)^(d))(γ_(z) _(t) ^(d) x _(t′) ^(d)+(1−γ_(x) _(t) ^(d)){tilde over (x)}^(d),

wherein: (i) γ_(d) _(t) ^(d) is the decay for the input value x_(t)^(d), x_(t′) ^(d)), is the last observation of the d^(th) variable, and{tilde over (x)}^(d) is the empirical mean of the d^(th) variable.W_(yx) may be constrained to be diagonal, effectively making decay rateindependent for each predictor. The other decay function may be used todecay a hidden state h_(t−1) according to:

ĥ _(t−1)=γ_(h) _(t) ⊙h _(t−1) _(t) ,

where the weight W_(yh) corresponding to the decay function h_(t−1) isnot constrained to be diagonal. In addition to the above modificationsto GRU 400, a masking vector may be added to GRU equations, usingspecial weights matrices such that inputs to GRU 400 may be given as:

R _(t)=σ(W _(r) [{circumflex over (X)} _(t) , ĥ _(t−1) ]+V _(r) m _(t)+B _(r))

Z _(t)=σ(W _(z) [{circumflex over (X)} _(t) , ĥ _(t−1) ]+V _(z) m _(i)+B _(z))

h′ _(t)=tan h(W _(h) [{circumflex over (X)} _(t) , R _(t) ⊙ĥ _(t−1) ]+V_(m) +B _(h))

h _(t)=(1−Z _(t))⊙ĥ _(t−1) +Z _(t) ⊙h′ _(i)

Accordingly, the modified GRU may take in a data set with missingvalues, masking vectors, and the modified inputs (as described above) tomake predictions. In other words, the foregoing approach may modify theinputs and the hidden states for a GRU using decay (which may becalculated using time interval and masking vector) and then suchmodified inputs, modified hidden state, and the masking vector may befed to the modified GRU.

The use of the modified GRU for prediction may have advantages over overLSTM and other known approaches for data imputation. For example, themodified GRU imputation approach described herein may be capable ofexploiting time-series nature of the training data, using the lastobservation, time since the last observation and the distribution of apredictor to make more accurate estimates for missing values of thetraining data. The use of the modified GRU imputation approach describedherein may assume no correlation and may only require a singleprediction step. In addition, the modified GRU imputation approachdescribed herein may enable combination of imputation and training intoa single step, eliminating the need for storing imputed datasets.Further, the additional computation cost associated with imputation inthe modified GRU imputation approach described herein may be at leastpartly offset by the low computation expense associated with GRUs whencompared to LSTMs.

As used herein, when two or more elements are referred to as “coupled”to one another, such term indicates that such two or more elements arein electronic communication or mechanical communication, as applicable,whether connected indirectly or directly, with or without interveningelements.

This disclosure encompasses all changes, substitutions, variations,alterations, and modifications to the example embodiments herein that aperson having ordinary skill in the art would comprehend. Similarly,where appropriate, the appended claims encompass all changes,substitutions, variations, alterations, and modifications to the exampleembodiments herein that a person having ordinary skill in the art wouldcomprehend. Moreover, reference in the appended claims to an apparatusor system or a component of an apparatus or system being adapted to,arranged to, capable of, configured to, enabled to, operable to, oroperative to perform a particular function encompasses that apparatus,system, or component, whether or not it or that particular function isactivated, turned on, or unlocked, as long as that apparatus, system, orcomponent is so adapted, arranged, capable, configured, enabled,operable, or operative. Accordingly, modifications, additions, oromissions may be made to the systems, apparatuses, and methods describedherein without departing from the scope of the disclosure. For example,the components of the systems and apparatuses may be integrated orseparated. Moreover, the operations of the systems and apparatusesdisclosed herein may be performed by more, fewer, or other componentsand the methods described may include more, fewer, or other steps.Additionally, steps may be performed in any suitable order. As used inthis document, “each” refers to each member of a set or each member of asubset of a set.

Although exemplary embodiments are illustrated in the figures anddescribed below, the principles of the present disclosure may beimplemented using any number of techniques, whether currently known ornot. The present disclosure should in no way be limited to the exemplaryimplementations and techniques illustrated in the drawings and describedabove.

Unless otherwise specifically noted, articles depicted in the drawingsare not necessarily drawn to scale.

All examples and conditional language recited herein are intended forpedagogical objects to aid the reader in understanding the disclosureand the concepts contributed by the inventor to furthering the art, andare construed as being without limitation to such specifically recitedexamples and conditions. Although embodiments of the present disclosurehave been described in detail, it should be understood that variouschanges, substitutions, and alterations could be made hereto withoutdeparting from the spirit and scope of the disclosure.

Although specific advantages have been enumerated above, variousembodiments may include some, none, or all of the enumerated advantages.Additionally, other technical advantages may become readily apparent toone of ordinary skill in the art after review of the foregoing figuresand description.

To aid the Patent Office and any readers of any patent issued on thisapplication in interpreting the claims appended hereto, applicants wishto note that they do not intend any of the appended claims or claimelements to invoke 35 U.S.C. § 112(f) unless the words “means for” or“step for” are explicitly used in the particular claim.

1. An information handling system comprising: a processor; and anon-transitory computer-readable medium having stored thereon a programof instructions executable by the processor, the program of instructionsconfigured to, when read and executed by the processor: receivetelemetry data associated with one or more information handlingresources; receive failure statistics associated with the one or moreinformation handling resources; merge the telemetry data and the failurestatistics to create training data; provide the training data to a gatedrecurrent unit; impute, by the gated recurrent unit, missing values fromthe training data; and train the gated recurrent unit, in accordancewith the training data, to predict a failure status of an informationhandling resource from operational data associated with the informationhandling resource.
 2. The information handling system of claim 1,wherein the training data comprises time series data generated from thetelemetry data and the failure statistics.
 3. The information handlingsystem of claim 1, wherein the program of instructions is furtherconfigured to, when read and executed by the processor, implement thepattern recognition engine as a recurrent neural network with the gatedrecurrent unit.
 4. The information handling system of claim 1, whereinthe program of instructions is further configured to, when read andexecuted by the processor, apply a rules-based decision engine to thefailure status to determine a remedial action for the informationhandling resource.
 5. A method comprising: receiving telemetry dataassociated with one or more information handling resources; receivingfailure statistics associated with the one or more information handlingresources; merging the telemetry data and the failure statistics tocreate training data; and providing the training data to a gatedrecurrent unit; imputing, by the gated recurrent unit, missing valuesfrom the training data; and training the gated recurrent unit, inaccordance with the training data, to predict a failure status of aninformation handling resource from operational data associated with theinformation handling resource.
 6. The method of claim 5, wherein thetraining data comprises time series data generated from the telemetrydata and the failure statistics.
 7. The method of claim 5, furthercomprising implementing the pattern recognition engine as a recurrentneural network with the gated recurrent unit.
 8. The method of claim 5,further comprising applying a rules-based decision engine to the failurestatus to determine a remedial action for the information handlingresource.
 9. An article of manufacture comprising: a non-transitorycomputer-readable medium; and computer-executable instructions carriedon the computer readable medium, the instructions readable by aprocessor, the instructions, when read and executed, for causing theprocessor to: receive telemetry data associated with one or moreinformation handling resources; receive failure statistics associatedwith the one or more information handling resources; merge the telemetrydata and the failure statistics to create training data; provide thetraining data to a gated recurrent unit; impute, by the gated recurrentunit, missing values from the training data; and train the gatedrecurrent unit, in accordance with the training data, to predict afailure status of an information handling resource from operational dataassociated with the information handling resource.
 10. The article ofclaim 9, wherein the training data comprises time series data generatedfrom the telemetry data and the failure statistics.
 11. The article ofclaim 9, the instructions for further causing the processor to, whenread and executed by the processor, the pattern recognition engine as arecurrent neural network with the gated recurrent unit.
 12. The articleof claim 9, the instructions for further causing the processor to, whenread and executed by the processor, apply a rules-based decision engineto the failure status to determine a remedial action for the informationhandling resource.